CloudQuery vs Dagster
CloudQuery and Dagster serve fundamentally different roles in the modern data stack. CloudQuery excels at extracting and querying cloud infrastructure data for security, compliance, and FinOps use cases. Dagster excels at orchestrating end-to-end data pipelines with asset-centric scheduling, lineage, and observability. The right choice depends on whether your primary need is cloud visibility or pipeline orchestration.
CloudQuery3.6Dagster4.3
Data Pipelines
Quick Comparison
| Feature | CloudQuery | Dagster |
|---|---|---|
| Primary Use Case | Cloud asset inventory, security, and compliance | Data pipeline orchestration and observability |
| Language | Go | Python |
| License | MPL-2.0 | Apache-2.0 |
| Pricing Model | Contact for pricing | Open-source self-hosted free (Apache-2.0), Solo Plan $10/mo, Starter Plan $100/mo, Starter $1200/mo, Pro and Enterprise Plan contact sales |
| GitHub Stars | 6,377 | 15,348 |
| Deployment | CLI (self-hosted) or managed platform | Self-hosted, Kubernetes, managed cloud, or hybrid |
CloudQuery
- Primary Use Case:
- Cloud asset inventory, security, and compliance
- Language:
- Go
- License:
- MPL-2.0
- Pricing Model:
- Contact for pricing
- GitHub Stars:
- 6,377
- Deployment:
- CLI (self-hosted) or managed platform
Dagster
- Primary Use Case:
- Data pipeline orchestration and observability
- Language:
- Python
- License:
- Apache-2.0
- Pricing Model:
- Open-source self-hosted free (Apache-2.0), Solo Plan $10/mo, Starter Plan $100/mo, Starter $1200/mo, Pro and Enterprise Plan contact sales
- GitHub Stars:
- 15,348
- Deployment:
- Self-hosted, Kubernetes, managed cloud, or hybrid
Community & Adoption Signals
| Metric | CloudQuery | Dagster |
|---|---|---|
| GitHub stars | 6.4k | 15.4k |
| PyPI weekly downloads | 2 | 1.6M |
| Docker Hub pulls | — | 5.2M |
| Search interest | 0 | 2 |
| Product Hunt votes | 5 | 302 |
As of 2026-05-04 — updated weekly.
Interface Preview
Dagster
Feature Comparison
| Feature | CloudQuery | Dagster |
|---|---|---|
| Core Capabilities | ||
| Cloud Asset Inventory | Full multi-cloud inventory with 50+ integrations across AWS, GCP, Azure, and SaaS tools | Not a core feature; requires external tools or custom integrations for cloud asset discovery |
| Data Pipeline Orchestration | Focused on extract-and-load from cloud APIs; no general-purpose pipeline scheduling | Full asset-centric orchestration with scheduling, partitioning, dependency management, and fault tolerance |
| Data Transformation | SQL-based policy queries and data normalization; no built-in transformation pipeline | Native dbt integration, Python transformations, and declarative asset-based transformation workflows |
| Data Quality | Policy-based validation for cloud configuration drift and misconfiguration detection | Built-in data quality checks, freshness monitoring, and automated validation embedded in pipeline code |
| AI and ML Support | AI-powered natural language query assistant for exploring cloud inventory data | Full ML pipeline orchestration for data prep, model training, experiment tracking, and AI applications |
| Observability and Monitoring | ||
| Data Lineage | Cross-resource relationship mapping showing connected cloud assets and dependencies | Built-in asset lineage graphs with dependency tracking across the entire data pipeline |
| Monitoring and Alerting | Event-driven triggers on drift, cost spikes, and security findings with webhook notifications | Intelligent Slack alerts, AI-powered debugging, impact analysis, and real-time health metrics |
| Data Catalog | Unified cloud asset catalog with normalized schema and metadata enrichment from 50+ sources | Integrated data catalog with asset documentation, ownership tracking, and cross-team discovery |
| Cost Visibility | FinOps integration for tracking cost allocation, identifying unused resources, and right-sizing | Built-in cost tracking and insights for monitoring data platform operational expenses |
| Platform and Operations | ||
| Integrations Ecosystem | Deep coverage for cloud platforms (AWS, GCP, Azure, Kubernetes) plus security and FinOps tools | Broad data stack integrations including Snowflake, BigQuery, dbt, Databricks, Spark, and Fivetran |
| Deployment Flexibility | CLI for self-hosted use or fully managed CloudQuery Platform | Self-hosted (single server or Kubernetes), managed Dagster Cloud, or hybrid deployments |
| Security and Compliance | Continuous compliance monitoring, security posture assessment, and SQL-based policy enforcement | SOC 2 Type II, HIPAA alignment, RBAC, SCIM provisioning, and audit logs for platform governance |
| Automation and Workflows | Event-driven workflows triggered by drift, cost spikes, or security findings with webhook support | Declarative scheduling, partitioned runs, sensors, and CI/CD-native branch deployment workflows |
| Enterprise Support | Tiered support plans (Free, Silver, Gold, Platinum) with SLAs up to 24/7 coverage | Dedicated enterprise support, private Slack channels, personalized onboarding, and uptime SLAs |
| Open Source | Open-source CLI under MPL-2.0 license, written in Go with 6,377 GitHub stars | Open-source under Apache-2.0 license, written in Python with 15,348 GitHub stars |
Core Capabilities
Cloud Asset Inventory
CloudQueryFull multi-cloud inventory with 50+ integrations across AWS, GCP, Azure, and SaaS tools
DagsterNot a core feature; requires external tools or custom integrations for cloud asset discovery
Data Pipeline Orchestration
CloudQueryFocused on extract-and-load from cloud APIs; no general-purpose pipeline scheduling
DagsterFull asset-centric orchestration with scheduling, partitioning, dependency management, and fault tolerance
Data Transformation
CloudQuerySQL-based policy queries and data normalization; no built-in transformation pipeline
DagsterNative dbt integration, Python transformations, and declarative asset-based transformation workflows
Data Quality
CloudQueryPolicy-based validation for cloud configuration drift and misconfiguration detection
DagsterBuilt-in data quality checks, freshness monitoring, and automated validation embedded in pipeline code
AI and ML Support
CloudQueryAI-powered natural language query assistant for exploring cloud inventory data
DagsterFull ML pipeline orchestration for data prep, model training, experiment tracking, and AI applications
Observability and Monitoring
Data Lineage
CloudQueryCross-resource relationship mapping showing connected cloud assets and dependencies
DagsterBuilt-in asset lineage graphs with dependency tracking across the entire data pipeline
Monitoring and Alerting
CloudQueryEvent-driven triggers on drift, cost spikes, and security findings with webhook notifications
DagsterIntelligent Slack alerts, AI-powered debugging, impact analysis, and real-time health metrics
Data Catalog
CloudQueryUnified cloud asset catalog with normalized schema and metadata enrichment from 50+ sources
DagsterIntegrated data catalog with asset documentation, ownership tracking, and cross-team discovery
Cost Visibility
CloudQueryFinOps integration for tracking cost allocation, identifying unused resources, and right-sizing
DagsterBuilt-in cost tracking and insights for monitoring data platform operational expenses
Platform and Operations
Integrations Ecosystem
CloudQueryDeep coverage for cloud platforms (AWS, GCP, Azure, Kubernetes) plus security and FinOps tools
DagsterBroad data stack integrations including Snowflake, BigQuery, dbt, Databricks, Spark, and Fivetran
Deployment Flexibility
CloudQueryCLI for self-hosted use or fully managed CloudQuery Platform
DagsterSelf-hosted (single server or Kubernetes), managed Dagster Cloud, or hybrid deployments
Security and Compliance
CloudQueryContinuous compliance monitoring, security posture assessment, and SQL-based policy enforcement
DagsterSOC 2 Type II, HIPAA alignment, RBAC, SCIM provisioning, and audit logs for platform governance
Automation and Workflows
CloudQueryEvent-driven workflows triggered by drift, cost spikes, or security findings with webhook support
DagsterDeclarative scheduling, partitioned runs, sensors, and CI/CD-native branch deployment workflows
Enterprise Support
CloudQueryTiered support plans (Free, Silver, Gold, Platinum) with SLAs up to 24/7 coverage
DagsterDedicated enterprise support, private Slack channels, personalized onboarding, and uptime SLAs
Open Source
CloudQueryOpen-source CLI under MPL-2.0 license, written in Go with 6,377 GitHub stars
DagsterOpen-source under Apache-2.0 license, written in Python with 15,348 GitHub stars
Our Verdict
CloudQuery and Dagster serve fundamentally different roles in the modern data stack. CloudQuery excels at extracting and querying cloud infrastructure data for security, compliance, and FinOps use cases. Dagster excels at orchestrating end-to-end data pipelines with asset-centric scheduling, lineage, and observability. The right choice depends on whether your primary need is cloud visibility or pipeline orchestration.
When to Choose Each
Choose CloudQuery if:
Choose Dagster if:
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Frequently Asked Questions
Can CloudQuery and Dagster be used together?
Yes. CloudQuery handles cloud infrastructure data extraction while Dagster orchestrates broader data pipelines. Teams often use CloudQuery as a data source within Dagster-orchestrated workflows, combining cloud asset inventory with downstream analytics and transformation pipelines.
Which tool is better for security and compliance monitoring?
CloudQuery is purpose-built for this use case. It provides continuous compliance monitoring, security posture assessment, audit-ready reports, and SQL-based policy enforcement across multi-cloud environments. Dagster does not offer native security monitoring features.
Which tool has better support for dbt and data transformations?
Dagster has a significant advantage here with native dbt integration, declarative asset definitions, and a full transformation orchestration layer. CloudQuery focuses on data extraction and loading rather than transformation workflows.
What are the open-source licensing differences?
CloudQuery uses the MPL-2.0 (Mozilla Public License) and is written in Go. Dagster uses the Apache-2.0 license and is written in Python. Both are fully open-source for self-hosted deployments, with paid managed platform options available.
Which tool scales better for enterprise use?
Both offer enterprise-grade capabilities but in different domains. CloudQuery scales for multi-cloud asset inventory with support tiers up to Platinum (24/7 SLA). Dagster scales for data pipeline orchestration with SOC 2 Type II compliance, HIPAA alignment, RBAC, and multi-tenant deployments.